The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II
SPATIAL RELATIONS AND INFERENCES FOR CONTEXT AWARE VISUALIZATION
O. Akcay and O. Altan
ITU, Faculty of Civil Engineering, Department of Geomatic Engineering, 34469 Maslak Istanbul, Turkey akcayoz@itu.edu.tr, oaltan@itu.edu.tr
Commission II, WG II/5
KEY WORDS: Ontology and Concepts in GIS, Location-Based Services, Web and Wireless Applications in GIS
ABSTRACT:
Data submission and refreshment, e.g. sensors, continuously feed a distributed information system. The system has to interpret
incoming data in order to get valuable information which comes from different sources. Mobile users need more adaptive visualized
spatial data according to location, device, personal profile, intention etc. In a semantic approach, an ontological model provides an
intelligent system which is capable of extracting implicit information from explicit one. In this paper, some spatial concepts and
properties have been defined for mobile devices and their users so as to produce context-aware visualization. The aim of these
mobile contextual ontologies is to obtain a semantic model which inferences some parameters for an appropriate visualization.
computing environment. Pervasive computing environments
gracefully integrated networked computing device - with people
and their ambient environments. A room, for example, might be
saturated with a lot of devices that provide information to
people without needing their active attention (Zhu et al., 2005).
Establishing a pervasive computing application has become one
of the major tasks in computer sciences. In particular, pervasive
computing demands applications that are capable of operating
in highly dynamic environments and of placing fewer demands
on user attention. In order to meet these requirements, pervasive
computing applications will need to be sensitive to context.
1. INTRODUCTION
Advanced wireless technologies enable smart applications on
Location Based Services (LBS). These smart applications are
capable of doing some inferences in order to obtain a
visualization which is relevant to the user situation. A
Geographical Information System (GIS) continuously receives
various kind of data and the system always updates itself.
Especially in systems like LBS, there are a lot of instant
changes that affect decision process. To evaluate effects of
instant changes accurately, knowledge-base systems should be
established. Establishing an appropriate knowledge-base system
requires some steps: Determining context, ontological approach
on the context and composing knowledge-base.
Depending on the advancement of pervasive computing,
geoinformation researches have tended to focus on contextaware and semantic modelling. Hong et al., 2005 proposed an
adaptive location data management strategy in order to support
adaptivity and scalability of the location based system using a
variety of context which can be accessed in the ubiquitous
computing environment. Hong et al., 2005, however, ignored
the need for a semantic projection of the context model in order
to obtain a ubiquitous computing system.
According to the context, a context aware system can be
provided. A context aware system is a system uses context to
provide relevant information and/or services to the user, where
relevancy depends on the user’s task (Dey and Abowd, 2000).
Ontological approach of the context should be implemented so
as to provide a system which can be interpretable by computers.
Ontological concepts and relations are coded with ontological
languages. A consistent knowledge-base provides a system
which is sensible and capable of showing reaction to the instant
changes.
Kim et al., 2005 presented the architecture of tour information
services based on semantic web technologies. The aim of the
service is to provide the exact tour information and
interoperability between the server systems. Nevertheless, the
definition of the ontologies of the tour information explains
only a small part of the location based services. It does not
provide a whole semantic system design. The tour ontology
should be integrated to user, device and spatial ontology and
relations with each other. Another ontology-based approach is
personalized
situation-aware
mobile
service
supply
(Weissenberg et al., 2006). The research, however, does not
include the visualisation styles of any spatial entity that
obtained at the context-aware service.
In this paper some spatial concepts and their relation have been
investigated in order to obtain relevant visualisation on small
mobile devices. Small mobile devices are not able to handling
huge amount of data. A simple computing environment
structure is necessary to process spatial data on tiny mobile
devices. Next section presents some related works. Section 3
then explains some concept, their relations and their some
simple inferences. Concluding remarks are discussed in last
section.
2. RELATED WORK
Chen et al., 2004 designed a context-aware architecture so as to
create intelligent spaces. They established a broker federation
formed by multiple brokers. Gu et al., 2004 also presented a
detailed context-aware architecture for smart home applications.
Scientifically, LBS is not only a spatial topic. Computer
Sciences also contribute to LBS, because they are pervasive
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 38, Part II
City A. Theatre A isPartOf District A. Theatre B isPartOf
District A and Theatre C isPartOf District C (Figure 2). We are
able to inference that Theather C and Person A are not at the
same district.
Christopoulou et al., 2004 defined an ontology-based context
model. Lutz and Klien, 2006 explained an ontology-based
Geographical Information retrieval contributes to solving
existing problems of semantic heterogeneity and hides most of
the complexity of the required procedure from the requester. In
this paper, as a different approach, modeling spatial
visualisation has been considered for any scale of a smart
application of LBS.
City A
District A
Ontological knowledge-based systems are composed with
ontology languages. Ontology Web Language (OWL) has been
accepted as a standard language of ontology (McGuinness and
van Harmelen 2004). The Semantic Web Rule Language
(SWRL) is another specification to extract implicit information
from explicit ones. SWRL concludes acquired knowledge with
a rule based XML syntax language. Therefore it can be
perceived a different kind of OWL-DL specification. In any
case, SWRL is based on a combination of the OWL-DL and
OLW-Lite sublanguages of OWL with the Unary/Binary
Datalog RuleML sublanguages of the Rule Markup Language
(RML) (Horrocks et al., 2004).
Buil. A
District B
Thea. A
District C
Thea. B
Thea. C
Con.Ro.
isPartOf
Pers. A
3. SPATIAL RELATIONS AND INFERENCES
Figure 2. Ontological relations.
Basic spatial objects are polygons, polylines and points.
Ontological perception of spatial objects should base on these
basic shapes. In two dimensional representation, buildings,
regions, territories, areas can be shown with polygons. Roads,
rivers, borders can be drawn with lines. Any other object can be
represented with point such as a bus station, a person and a
vehicle. Figure 1 depicts a simple map with basic spatial
objects.
District A is the lowest common level of the Person A, Theatre
A and B. Theatre C isPartOf District C. Theatre A and Theatre
B are the closest theatres to Person A. Now one question remain
that which theatre is the closest to Person A in District A. To
answer this question by ontologically, smaller entities than a
district should be created. partsIn this method, areas (polygons)
should be divided small parts to get realistic results to the
queries (Figure 3).
District A
District A
A1
Theatre A
Building A
1
2
1
Figure 1. A basic map.
A1 is an instance of Building Concept. A1 building has to be in
a district of the city. Building A1, therefore, is subsumed by a
region. Let us assume that District A subsumes Building A1. A
district is subsumed by a city. A city is subsumed by a country.
A country is subsumed by a continent. A continent is subsumed
by the world. Consequently everything which has dimensions is
a part of the world. This ontological concept can be extended to
three dimension.
2
Theatre B
3
LBS is a smart environment which can be established in
different scales. It can be valid for either a home or a city.
Smart environments are so complex for wide areas. It needs
well organised ontological relations. Otherwise computer
cannot handle huge amount of interactions occurred at the same
time.
Figure 3. Retrieving the neighbours of Building A by using
isNextTo in District A.
Let us assume that person A stays in the conference room. He
or she wants to go to a theatre close to him. Spatial concepts
connected each other with relation of isPartOf. Person A
isPartOf Conference Room. Conference Room isPartOf
Building A. Building A isPartOf District A. District A isPartOf
isNextTo relation defines neighbours of small District parts.
Building A has two isNextTo relation to Theatre A whereas
there is three relation to Theatre B. The fewer relation numbers
show the closest places. Consequently Theatre A therefore can
be obtained as the closest to Building A.
isNextTo
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McGuinness, D.L. and van Harmelen, F., 2004. OWL web
ontology language overview, W3C Recommendation,
http://www.w3.org/TR/owl-features/, 2004.
4. CONCLUSION
Ontological approach including concepts, relations and
instances is essential for smart environments like LBS. This
paper presents a simple spatial ontology in order to provide a
location sensitive context aware computing system. System
enables a solution for finding closest place at the smart
environments. The solution differs from distance based
solutions because of its applicable data structure to an
ontological context aware system.
Weissenberg, N., Gartmann, R. and Voisard, A., 2006. An
ontology-based approach to personalized situation-aware
mobile service. GeoInformatica 10, pp. 55–90.
Zhu, F., Mutka, M. and Ni, L., 2005. Service discovery in
pervasive computing environments. Pervasive Computing 4, pp.
81– 90.
This ontological structure should be extended to handle more
LBS components such as navigation and meeting queries.
Ontological inferences provide implicit context so as to obtain
additional information about the situation.
Statistical analyses of the ontological concepts and their
relations have not been completed yet. After the analysis, some
obvious benefits of the knowledge base also will be shown
later. This paper also ignores roads to determine closest place.
To obtain more accurate result road entity should be also added
to knowledge base.
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